THE POSITION GENERATOR IN THE NEPS - BENJAMIN SCHULZ, ANDREAS HORR, AND KERSTIN HOENIG NEPS SURVEY P APERS

 
neps Survey papers

Benjamin Schulz, Andreas Horr, and Kerstin Hoenig
The Position Generator in the NEPS

NEPS Survey Paper No. 23
Bamberg, May 2017
Survey Papers of the German National Educational Panel Study (NEPS)
at the Leibniz Institute for Educational Trajectories (LIfBi) at the University of Bamberg

The NEPS Survey Paper Series provides articles with a focus on methodological aspects and data
handling issues related to the German National Educational Panel Study (NEPS).

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and NEPS.

They are of particular relevance for the analysis of NEPS data as they describe data editing and data
collection procedures as well as instruments or tests used in the NEPS survey. Papers that appear in
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The NEPS Survey Papers are available at https://www.neps-data.de (see section “Publications“).

Editor-in-Chief: Corinna Kleinert, LIfBi/University of Bamberg/IAB Nuremberg

Contact: German National Educational Panel Study (NEPS) – Leibniz Institute for Educational
Trajectories – Wilhelmsplatz 3 – 96047 Bamberg − Germany − contact@lifbi.de
The Position Generator in the NEPS

                     Benjamin Schulz, WZB Berlin Social Science Center

       Andreas Horr, Leibniz Institute for Educational Trajectories (LIfBi) and
             Mannheim Centre for European Social Research (MZES)

          Kerstin Hoenig, Leibniz Institute for Educational Trajectories (LIfBi)

E-mail address of lead author
Benjamin.Schulz@wzb.eu

Bibliographic data:
Schulz, B., Horr, A., & Hoenig, K. (2017). The position generator in the NEPS (NEPS Survey
Paper No. 23). Bamberg, Germany: Leibniz Institute for Educational Trajectories, National
Educational Panel Study.

NEPS Survey Paper No. 23, 2017
Schulz, Horr, & Hoenig

The Position Generator in the NEPS
Abstract
The position generator is an established survey instrument to measure the status
composition of actors’ egocentric social networks. This paper sets forth the theoretical
background of the position generator by providing a brief overview of social capital theory
and the mechanisms through which social capital can affect educational outcomes. Against
this background, it details the version of the position generator implemented in all NEPS
starting cohorts. The NEPS position generator is intended to measure respondents’ social
resources by giving a range of 13 professions and asking respondents whether they
personally know someone who pursues this profession and if so, about the ethnic
background of this person. We analyzed the scientific use files of four NEPS starting cohorts
to test the reliability and validity of the instrument and to describe corresponding
distributions. Results show that the instrument performs well over all starting cohorts. We
found consistent differences between migrants’ and natives’ social networks in terms of
number of contacts, mean status composition and ethnic composition of the network.
Likewise, respondents’ occupational status is associated with their social network’s status
composition. Finally, we examined changes in network composition over a four-year span
and found that networks do change over time, with a propensity to increase as respondents
age and advance in their careers. We are confident that the position generator is well suited
to measure differences in respondents’ social capital and to examine inter-group differences.

Keywords
social capital, position generator, social networks

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1. Introduction
Since their development in the 1980s, social capital theories made a stellar career in the
social sciences. Scholars use them to better understand educational inequality, status
transmission, labor market integration but also group coherence and political participation
(cf. e.g., Brandt, 2006; Coleman, 1988; Dufur, Parcel & Troutman, 2013; Krug & Rebien,
2012; Moerbeek & Flap, 2008; Morgan & Todd, 2009; Mouw, 2006; Weiss & Klein, 2011;
Yakubovich, 2005). Moreover, social capital has been identified as a crucial factor for
immigrant integration, especially for educational attainment and labor market integration
(cf. esp., Portes, 2008). A key reason for the steep career of ‘social capital’ is that—under the
same label—different concepts have been developed simultaneously in different fields. The
concepts proposed by Bourdieu (1986), Burt (1992, 2001), Coleman (1988, 1990), Lin (1982,
2001), and Putnam (1993, 1995, 2000) are probably the most influential contributions, each
of them inspiring a vast number of empirical applications (for reviews, cf. e.g., Adam &
Roncevic, 2003; Castiglione et al., 2008; Flap, 1999; Field, 2008; Lin & Erickson, 2008; Van
Deth, 2003).

Despite meaningful differences between the various concepts of social capital, there is one
significant commonality: Social capital is usually defined with reference to the resources
accessible through social networks. The common thread of the various social capital
concepts is that networks give access to some kind of resource, which individuals can use to
achieve their goals. Besides the configuration of networks, the resources that one’s contacts
possess are the main determinant of social capital (see, e.g., Lubbers et al., 2010). Social
capital is thus contingent on the economic, human, and cultural capital as well as the
prestige, status, and power of one’s contacts (cf. esp., Bourdieu, 1986).

Given the link between the resources controlled by an actor’s contacts on the one hand and
her endowment with social capital on the other, it is hardly surprising that the distribution of
social capital tends to mirror the distribution of other forms of capital. This fact is a result of
both preferences for relationships with others who are similar to oneself and more
opportunities to meet similar others, for instance, in school or at the place of work. Inter-
personal networks, consequently, tend to be homogeneous in terms of attitudes, world
views, and lifestyles and in terms of education and socio-economic status. Members of the
middle or upper class, accordingly, tend to be embedded in networks with a higher socio-
economic status than members of the lower classes (Bourdieu, 1986; Lin, 1999, 2001).

Lin (2001) summarized social inequality in endowment with social capital in his strength-of-
position proposition:

         “This proposition predicts a structural effect on social capital: those in better social positions will have
         the advantage in accessing and mobilizing social ties with better resources.” (Lin, 2001: 65)

According to this proposition, actors in higher socio-economic positions not only have access
to more social resources but can also mobilize them more easily than actors in lower
positions. Lin (1999, 2001), moreover, underlines that the effect of an actor’s socio-
structural positioning on her social capital endowment is stronger than the well-known
effects of network configuration, such as the strength of (weak) ties.

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In light of the importance of socio-economic network composition for one’s social capital
endowment—and its effects on individual outcomes such educational attainment, labor
market participation, or status transmission— a specific survey instrument has been
developed: the position generator (cf. esp., Lin & Dumin, 1986; Lin & Erickson, 2008; Lin, Fu
& Hsung, 2001; Van der Gaag, Snijders & Flap, 2008). It measures the status composition of
an actor’s network by providing respondents with a list of occupations of different social
status and asking them to indicate whether they know anybody in that occupation. Under
the assumption that contacts in higher socio-economic positions control more valuable
resources, it therefore provides us with an indirect estimate of the social resources
accessible through an individual’s wider network.

In this paper, we present the NEPS position generator. In the next section we set forth its
theoretical background. Against this background, we discuss the fundamental
methodological concerns that we considered in the development of the instruments and
provide an overview of how the position generator is implemented across the different NEPS
starting cohorts and the NEPS social capital framework (section 3). Finally, we describe the
data as available in the Scientific Use Files of several NEPS cohorts by now and draw
conclusion along with some suggestions for the usage of the instrument.

2. Theoretical background
Social capital has been conceptualized in very different ways by scholars such as Bourdieu
(1986), Coleman (1988), Granovetter (1973), or Lin (1999). In light of the diversity in its
origins and frameworks, it is not surprising that social scientists mean quite different things
when they refer to ‘social capital’—a particularly large amount of conceptual ambiguity has
often been criticized (cf. e.g., Adam & Roncevic, 2003; Esser, 2008; Van Deth, 2003). In order
to reduce this ambiguity, it is helpful to differentiate between a collective and an individual
concept of social capital (cf. e.g., Esser, 2008; Kriesi, 2007). Collective social capital covers
the degree of trust, social control, cooperativeness, and the validity of norms in social
collectives. In the second perspective, (individual) social capital is considered as a relation-
based social resource. It covers, for instance, the exchange of information or support among
friends. As the concept underlying the position generator is individual social capital, we focus
on this form here.

Individual social capital refers to the resources that actors can access through the inter-
personal networks and groups they belong to—and which they can use as productive means
to achieve their goals. Individual social capital is a resource that actors can use to create
(more) benefits (Lin, 1999: 30; Snijders, 1999: 29). The probably most cited definition of this
form of social capital reads as follows:

         “Social capital is the sum of the resources, actual or virtual, that accrue to an individual or a group by
         virtue of possessing a durable network of more or less institutionalized relationships of mutual
         acquaintance and recognition.” (Bourdieu & Wacquant, 1992: 119)

Inter-personal networks are the basis of individual social capital as it arises from an actor’s
relationships with others. It is a personal asset, although not controlled by the individual
actor alone. Rather, it is situated in the relations between actors (Coleman, 1990; Loury,
1977).

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2.1 Forms of social capital
Applying a broad definition of social resources, we identify six forms of social capital that
represent five different ways how social capital operates. These five ways are: i) information,
ii) instrumental support, iii) social credibility and credentials, iv) emotional support and
social recognition, and v) the norms, values, and orientations predominating in a certain
network or group. In the following paragraphs, we will briefly explain how these six forms of
social capital function.

First, information is an important resource that flows through inter-personal networks.
Actors often get to know about new opportunities through their social relations. Moreover,
they gain insights into processes they would otherwise not understand. Parents, for
instance, who are looking for a kindergarten place for their child, may receive information
about new vacancies through their inter-personal networks. Erikson and Jonsson (1996)
explain that information about the functioning of the school system contributes to the
generation of educational inequality. Similarly, Dufur, Parcel, and Troutman (2013) argue
that families with low levels of social capital provide less information and thus impair
children’s academic achievement. Information gathered through inter-personal ties reduces
transaction costs. If friends or family members, for instance, keep their ears open and
forward information on vacancies in childcare facilities to the person in question, that
person saves time and search costs. Second, network-based information is particularly likely
to lead to better matches between applicants and positions (see esp., Dustmann, Glitz,
Schönberg & Brücker, 2015).

A second form of individual social capital is instrumental support or practical help. It can take
many forms. A grandmother who regularly takes care of her grandchildren supports her
daughter’s career. Support means that others use their resources in order to enhance an
actor’s returns or to create benefits for him. This primarily refers to using time and the
commitment of specific skills. In most cases, others’ support directly reduces an actor’s
costs, which, in turn, gives her more leeway for achieving other goals.

The third form of individual social capital is the social credibility and credentials accruing
from one’s inter-personal networks. Whereas support yields returns through reducing an
actor’s costs and direct influence on third parties, social credibility works indirectly.
Networks, especially mutual acquaintances, function as both a leap of faith and a
reassurance for third parties. Inter-personal contacts, especially mutual acquaintances,
signal an actor’s trustworthiness and social acknowledgement—that is, they serve as an
affirmation of his creditworthiness and soundness. What matters for our discussion of the
position generator is the fact that the credibility endorsed by a certain recommendation is a
function of the prestige, status and positioning of the persons that provides it.

Fourth, emotional support and recognition is a further form of social capital that is
particularly important for mental and physical health (Ferlander, 2007). It functions by
strengthening an actor’s identity, self-worth and self-efficacy beliefs as well as his motivation
and commitment. In this way, it also has indirect effects on educational and labor market
success, as better mental health strengthens one’s capability and employability. Emotional
support is almost exclusively provided by strong ties: It is one’s inner circle (partners, close
family members, and friends) that usually provides attention, love, and security that

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strengthens and stabilizes one’s self-esteem. Strong ties make an actor feel that he is
recognized and cherished, which is particularly important in difficult times, such as in case of
severe illness or personal crises (Lüdicke & Diewald, 2007: 266f).

The norms, values and orientations that predominate in a certain group or network
constitute a fifth form of social capital. Whereas the first four forms deal with resources in
the narrow sense, this fifth form rests on a broad, rather metaphorical, understanding of
resources. Norms and values primarily affect individual beliefs and motivations. Such effects
can even (mis)lead actors to disregard other situational aspects or to ignore the
consequences of a decision. In this way, networks can enforce educational aspirations or
establish a normative environment that supports educational attainment (Coleman, 1988;
Dijkstra et al., 2003; Thorlindsson et al., 2007; Sewell et al., 1970; Singer, 1981; Stocké et al.,
2011). Group norms, however, are not necessarily positively interrelated with educational
achievement and attainment—they can also transmit counter-productive norms or anti-
social behavior, for instance, by promoting futility cultures (Agirdag, Van Houtte & Van
Avermaet, 2012). Effects of group norms on individuals largely stem from the adoption of
attitudes and behaviors of role models or the (often unconscious) internalization of peer
norms. Group identification and a group’s power to sanction behaviors that violate group
norms are important conditions that influence the efficiency of reference group effects
(Portes & MacLeod, 1999).

All five forms of social capital rely on resource transmission. As the diversity and subtleties of
the five forms already suggest, it is difficult to develop direct measurements of social
resources—in large-scale surveys, in fact, it is a hardly feasible task. The position generator
therefore takes another approach: it indirectly assesses social resources through the status
composition of the social network. It stems from the idea that control over resources is
directly linked to an actor’s position in the social hierarchy. According to Lin (2001), the
social hierarchy takes the shape of a pyramid – a few actors in the positions at the top
control the most valued resources, whereas the most common positions at the bottom
control the least valuable resources. Ties to actors with a higher status therefore promise
higher benefits, but ties to actors with a similar or lower status are more easy to establish
and maintain due to the fact that these positions are more common and due to the
homophily principle.

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                         High status                                High status

                                                                                     upper reachability

                                 Social ties        heterogeneity                 extensity
           Ego

                         Low status                                 Low status

Figure 1: Ties within the status hierarchy and measures of social capital (Lin, 2001: 62)

Lin (2001: 62) suggests that an actor’s endowment with social capital can be measured by
three network characteristics: upper reachability, heterogeneity, and extensity (see Figure
1). Upper reachability refers to the highest social tie in the status hierarchy. In general, the
five forms of social capital described above tend to be associated with the positioning or
status composition of an actor’s network in the same (positive) way: The higher the socio-
economic positioning, the more resources of information and support are available; the
higher the status of one’s contacts, the more influence they can exert on others and the
more social credibility is conferred. The same usually holds true for norms, values, and
orientations regarding learning, education and career orientations: Such norms tend to rise
with the socio-economic positioning of one’s network.

However, not all social resources require high-status contacts; some resources are provided
more cheaply and efficiently through ties to others who are of equal or lower social status.
Examples include information about school events from a fellow parent, help with caregiving
duties from the neighbor’s child, or emotional support from close friends. It is therefore
beneficial to have access to positions from different points in the status hierarchy. This
characteristic is captured by the measure of network heterogeneity, which refers to the
vertical range of positions that are accessible through social ties. The third measure,
extensity, finally, captures the total number of positions that can be reached by an actor.
Ceteris paribus, social capital increases with the number of positions that can be reached,
because the number of accessible resources grows with each additional contact.

The first two measures, upper reachability and network heterogeneity, are directly linked to
the well-known finding that ties to other social circles are particularly beneficial for both
individuals and societies at large (cf. esp., Granovetter, 1973; Burt, 2001). Bridging ties,
defined as those inter-personal relations that are the only links between otherwise
disconnected groups or networks, are particularly valuable because they tend to connect
actors that have less in common than actors in closely knit networks. They are, therefore,
more likely to make available non-redundant information and disseminate news, for
instance, about job openings. Moreover, bridging ties give access to further (scarce) goods

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such as (affordable) housing or child care facilities. Bridging ties thus increase opportunities
for individual and collective action by facilitating—or even enabling—interaction among
distant actors.

The advantage of ties that bring together distant actors and groups is also critical for the
differentiation between bridging and bonding social capital (Putnam, 2001). The crucial
difference between the two concepts is exactly what we have just described: Wide networks
provide actors with more diverse social resources than narrow ones. Bonding social capital,
by contrast, refers to the idea that dense networks have a particular strength as well—they
support the mobilization of social resources, especially of costly ones, which accrues from
the capacity of dense networks to facilitate social control, (credits of) trust, shared identities,
and solidarity.

2.2 Immigrant social capital: How ethnic communities matter
The distinction between bridging and bonding social capital is particularly prominent in
integration research, especially in studies investigating the socio-economic incorporation of
immigrant groups. For immigrants, bridging social capital is then mostly equated with having
inter-ethnic ties, that is, ties to the majority population. Likewise, bonding social capital is
equated with having intra-ethnic ties, that is, ties within the co-ethnic community (e.g.,
Heizmann & Böhnke, 2016; Koopmans, 2016). This distinction is based on the (well
confirmed) assumption that immigrant groups are positioned in the lower rungs of the socio-
economic hierarchy, thus providing them with less social capital. Inter-ethnic ties, by
contrast, indicate bridging social capital because they connect immigrants with more distant
others, who also tend to be better situated than co-ethnics. Inter-ethnic ties accordingly
provide immigrants with more social capital, which, again, might strengthen socio-economic
incorporation through all five ways discussed above.

Besides these general forms of social capital, the impact of further immigrant-specific forms
of social capital on immigrant integration is vigorously debated in integration research.
These specific forms mainly deal with the effects of ethnic communities that have always
been identified as important factors shaping incorporation processes (see esp., Portes &
Rumbaut, 2001). We identify four forms of immigrant-specific social capital (for the sake of
simplicity we will refer to them as ethnic social capital).

First of all, the ethnic community is particularly important for newly arriving immigrants as it
helps them to find guidance despite language barriers. The ethnic community offers easy
access to crucial information, especially about typical matters that immigrants face upon
arrival in a new environment, such as job search, (affordable) housing, administrative
matters, specific shopping facilities, or medical aid. This first form of ethnic social capital
largely follows from being able to communicate with each other. This argument, however, is
not only about language barriers. It is also concerned with mutual understanding (e.g., of
codes, customs, and modes of reaction)—it is linked to socio-cultural characteristics. Last but
not least, fellow co-ethnics can more naturally put themselves in the position of newly
arriving immigrants as they often share similar (defining) experiences—many community
members once happened to be in similar situations.

Second, in response to the particular challenges that immigrants face, ethnic communities
often develop particular institutions or organizations that help them to meet these

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challenges. Prime examples of such organizations are cultural associations, religious
communities, and youth centers, often headed by social workers from the community (Zhou,
2009). These centers are not only successful because they offer homework assistance and
specially tailored activities that meet the needs of young immigrants but also because they
are sensitive to group-specific norms and values—thereby, gaining the acceptance of both
immigrant youth and parents.

Third, ethnic networks and communities can have protective effects. In particular, they can
prevent adolescents from negative contextual influences, especially from adopting the
counterproductive attitudes or behavior of disadvantaged groups with whom they come into
contact. Immigrants do so more often than natives as they are more likely to grow up in
disadvantaged neighborhoods. In contrast to others, ethnic networks are particularly likely
to become protective shields against deprivation. This is because they tend to be denser
than other networks, which fosters intergenerational closure and facilitates both social
control and the enforcement of group norms (Portes & Rumbaut, 2001).

Fourth, for similar reasons ethnic communities might be able to promote achievement
norms and values that favor educational and occupational attainment. Again, such
arguments have mainly been pushed forward for the U.S., where Asian-Americans are seen
as the prime example of (educational) success against the odds (cf. e.g., Zhou & Bankston,
1994; Zhou & Xiong, 2005). Similarly, Shah, Dwyer & Modood (2010) identify shared norms
in Pakistani families in the U.K. as crucial factors that help understand why Pakistani student
aim for better education than other groups.

Fifth, ethnic networks and communities may also be conducive to socio-economic
advancement indirectly—through psychological mechanisms. They can reinforce identities,
thereby, strengthening self-esteem and mental health, which, in turn, helps keep alive
educational and occupational aspirations and motivations—even in hostile environments,
that is, in case of discrimination or exclusion. Shared ethnic identities, moreover, strengthen
ethnic solidarity, especially if they are endorsed by a sense of common destiny. Ethnic (in-
group) solidarity, then, lets minority group members become more willing to share their
resources with others. In other words, solidarity eases the mobilization of all kinds of social
capital.

As our brief discussion of the five forms of immigrant social capital has already shown,
whether or not ethnic networks and communities, eventually, foster socio-economic
integration is conditional on both the resources and norms predominating in a community
and corresponding network structure. Ethnic communities are neither solely a springboard
nor solely a trap, as Zhou (2009: 1156) put it—, there rather exists a varied “spectrum of
resources and constraints” that influence immigrant incorporation. Some effects even
depend on further contextual factors, especially on the (non-)existence of ethnic boundaries
and social acceptance by the majority population as well as on integration regimes (e.g.,
regulation of labor market access). This conditional view regarding the influence of ethnic
networks and communities challenges us to specify much more precisely which resources,
orientations, and values ethnic communities actually provide—and when they influence
integration outcomes.

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In order to make progress here, we need both measures of the different forms of social
capital and measures of the ethnic composition of the corresponding networks. Having both
of them allows us to differentiate between general forms of social capital and immigrant-
specific ones. Since it is not feasible to measure the various forms of immigrant social capital
directly, we need to include ethnic network composition as a proxy measure for identifying
‘ethnic residuals’ that might point to the significance of immigrant-specific social capital. The
special focus of NEPS on education acquisition with a migration background (cf. Kristen et al.,
2011) and the apparent lack in social capital data, were a main reason for the development
of a comprehensive NEPS social capital instrument, especially for including measures of
ethnicity in the position generator (for details, cf. Hoenig et al., 2016).

In order to be able to test the impact of social capital—and to evaluate its role in the
generation of inter-group differences in educational achievement and attainment or labor
market integration, it is crucial to obtain sound measurements. The position generator is
designed to provide a reliable measurement of wide inter-personal networks. By providing a
list of positions of different socio-economic status, the position generator can be used to
measure the upper reachability, heterogeneity and extensity of an actor’s network. This fact
is the main reason why we implemented a short version of the position generator across the
NEPS cohorts. In order to be able to distinguish between the general social capital
mechanisms discussed in this section and the specific ways how ethnic networks function,
we additionally asked for the country of origin of all persons indicated by the respondent in
the position generator. In the following section we describe our measurement instrument
and how we developed it.

3. Measurement concept and methodological concerns
In large-scale surveys, social capital is usually measured based on the survey participant’s
ego-centered network. In ego-centered measurement instruments, the individual
respondent herself is the source of information about her social networks. She is regarded as
a focal actor F who is connected to other actors A1 to Ak (alteri) via social relations r. These
alteri along with their connections to ego form F’s ego-centered network. Such approaches
have long been implemented in survey research (see already, Burt, 1984; Fischer, 1982;
Marsden, 1990). From the long list of social capital instruments that have been proposed,
the most common ones1 are (Häuberer, 2011; Van der Gaag & Webber, 2008): i) name
generators, ii) the resource generator, and iii) the position generator. All these measurement
instruments are part of the comprehensive NEPS social capital instrument. The rationale,
layout, and development process of the NEPS social capital instrument has been described
by Hoenig et al. (2016). In what follows, we confine ourselves to presenting the NEPS
position generator as an integral part of the NEPS social capital instrument.

3.1 The measurement instrument in general
The position generator is a widely used social capital instrument (Lin & Dumin, 1986; Lin et
al., 2001; Lin & Erickson, 2008). In contrast to other ego-centered social capital instruments,
it is not meant to gather detailed information about particular contact persons but measures
the status composition of an actor’s wider network. It does so by surveying the ties of survey

1
 In their meta-analysis, Hlebec & Kogovsek (2013) also cover ‘role generators’ and ‘event-related support networks’. As these two have a
very particular focus and have not been considered for implementation in NEPS, we do not take them into consideration here.

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respondents to persons in certain occupations. Respondents are asked to indicate from a list
of occupations whether or not they personally know someone who pursues such a
profession. These occupations range from low-status occupations, such as security guards or
warehousemen, to high-status professions, such as lawyers or physicians. The approach is
based on the assumption that occupations can be ranked according to the social status they
entail—and that social status involves control over material and immaterial resources.

Social capital indicators derived from the position generator “are based on the idea that the
occupations of network members represent social resource collections that can be
quantified with job prestige measures” (Van der Gaag & Webber, 2008: 39). Accordingly,
prestige scores are allocated to each profession in empirical application. Mostly, the Socio-
Economic Index of Occupational Status (ISEI, cf. Ganzeboom, Degraaf & Treimann, 1992) is
used. Depending on theoretical interests or substantial hypotheses, researchers can
generate various social capital indicators, such as the mean ISEI of all professions reached or
the heterogeneity of positions. The position generator, thus, facilitates mapping the status
composition of ego’s wider network and available resources.

The position generator usually refers to weak ties. Such ties may include distant
acquaintances, (former) colleagues as well as fellow members of clubs or other organizations
to which someone belongs. Some versions of the position generator collect further
information about the persons that obtain a certain occupation. Such details include the
frequency of contact, how well the persons know each other, or the kind or strength of a
relationship. If one does not add too many contextual questions, the position generator is a
(time-) efficient measurement instruments with particularly low interviewee and interviewer
burden (Chua, Madej & Wellman, 2011: 107). Interviewer misconduct and panel
conditioning are therefore rather unlikely.

The position generator, of course, has its weaknesses as well. First of all, as it is designed to
measure those aspects of social capital that influence instrumental actions, such as job
search or occupational attainment, the position generator is not well-suited for covering
those social capital dimensions that refer to expressive action (e.g., emotional support or
recognition) (Häuberer, 2011: 140). Second, the social capital indicator gained through the
position generator depends on the professions included in the list of occupations. At the
same time, this list is sensitive to the concrete labeling of occupations. Accordingly, positions
(and labels) need to be chosen in a way that suits all subgroups of the study population. This
is by no means a given. Gendered labor markets, for instance, can bias results between men
and women if the jobs listed in the position generator tend towards one group, that is, if
there are primarily male or female dominated jobs in the list of occupations. Similarly, it has
been shown that the position generator is less applicable for homemakers, pensioners or
unemployed respondents than for those who are in the labor force (Häuberer, 2011: 141).
Third, inter-cultural differences in norms or gender role beliefs can affect indicators derived
from the position generator. Last but not least, although the position generator is specifically
geared to measuring resource possession, it does so in a broad and indirect way—by
ascribing prestige scores to occupational positions. Resource exchange or actual resource
mobilization is not measured. The position generator is therefore of little help if one seeks to
specify the exact channels through which social capital has an effect on certain outcomes.

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3.2 The measurement in the NEPS
The position generator is an integral part of the NEPS social capital instrument. It includes
measures of prospective and retrospective access to specific social resources, questions
about the expectations and aspirations of friends, family members and peers, and parental
involvement in their child’s education. The latter instruments address more directly through
which pathways social capital affects educational and labor market outcomes, but they are
usually targeted to specific educational or labor market outcomes and lack the general scope
of the position generator. Our overall strategy thus aims to combine the strengths of the
position generator and more direct measures of resource access and mobilization. An
overview of social capital instruments within NEPS can be found in Stocké et al. (2011). For
an overview of the process of development, see Hoenig et al. (2016).

It is assumed that networks of professional contacts develop when actors enter the labor
market and that actors rely on parental networks before that time (see Granovetter, 1974).
Therefore, the position generator is administered to parents while respondents are still in
school and only administered to respondents directly once they have left school.

In general, we try to include measures of the position generator shortly before important
transitions take place, such as the transition to elementary school in Starting Cohort 1 or the
transition into vocational training in Starting Cohort 4. In Starting Cohort 6, which tracks
adults of all age groups, most of whom have finished formal education, we measure the
position generator every four years. In Starting Cohorts 4 and 5, which track young adults
through vocational training or university and then into the labor market, we plan repeated
measures of the position generator around the time when subjects make the transition into
the labor market. It will thus be possible to examine the development of respondents’
professional networks and (for Starting Cohort 4) compare it with the parental network. An
overview of realized and planned schedule for all Starting Cohorts can be found in Table 1.

Table 1: Schedule for position generator measurements in all NEPS Starting Cohorts

Starting   2009     2010      2011     2012      2013     2014      2015    2016    2017    2018    2019     2020
Cohort

1                                                                                            P
                                                                                            (W7)
2                                                  P
                                                  (W3)
3                                                                     P                               T
                                                                     (W6)                           (W10)
4                     P                                     P                                 T
                     (W1)                                  (W7)                             (W11)
5                                        T                                    T                                T
                                        (W4)                                (W11)                            (W18)
6           T                                      T                                  T
           (W2)                                   (W6)                              (W10)

T: Position generator administered in target person interview
P: Position generator administered in parent interview
Entries for 2018-2020 reflect current plans and may be subject to change

NEPS Survey Paper No. 23, 2017                                                                              Page 12
Schulz, Horr, & Hoenig

We included a short version of the position generator in NEPS studies. Its basic shape comes
close to common versions (cf. e.g., Lin, 1999) as sketched above. The biggest difference
between the position generator implemented in NEPS and other versions is the number of
occupational positions covered. Whereas a complete version of the position generator
usually covers 30 to 40 occupations, our NEPS instrument covers only 13. This is a result of
time restrictions. Developing a short position generator for the NEPS that would last no
longer than 1.5 minutes, we referred to a version of the instrument that has been developed
for the project “Young Immigrants in the German and Israeli Educational System” (for
details, cf. Roth, Salikutluk & Kogan 2010; Kalter et al., 2013). Based on cognitive pretests
and pilot studies, we adapted the labeling of several occupations and their ordering (for
details about the development process and pilot studies, see Hoenig et al., 2016).
Furthermore, we refined the ethnic network composition measurement: For each
occupational position selected, the respondent is asked to indicate from which country the
person occupying that position originates. This allows us to construct measures of both the
share of alteri with a migration background and the share of co-ethnics in the respondent’s
weak tie network.

The NEPS position generator was employed in several NEPS-studies, both for the studies’
main targets person as well as student’s parents in cohort 2 and 4. Currently, data for the
position generator are available in the Scientific Use Files for starting cohort 2, 4, 5 and 6:

•        SC2, kindergarten (Parents), wave 3 (2013), CATI

•        SC4, students in grade 9 (Parents), wave 1 (2010), CATI

•        SC5, first-year academic students (Target), wave 4 (2012), CAWI

•        SC6, adults (Target), NEPS wave 1 (2009/2010), CATI/CAPI

•        SC6, adults (Target), NEPS wave 5 (2013/2014), CATI/CAPI

The position generator was, to a large extent, measured consistently over these cohorts and
waves with slight differences in wording, e.g. due to the different survey modes.
Respondents were given a list of professions and were asked whether there is someone in
their social circle (e.g. partner, family, relatives, friends, colleagues or other acquaintances)
who currently works in this profession. If they answered that such a person exists, they were
asked about this person’s country of origin (or the person closest to them, if there was more
than one).

NEPS Survey Paper No. 23, 2017                                                            Page 13
Schulz, Horr, & Hoenig

Table 2 describes the instrument as it was typically employed and gives English translations.2

2   Please note that the English translations given in the NEPS Scientific Use Files might differ slightly.

NEPS Survey Paper No. 23, 2017                                                                                Page 14
Schulz, Horr, & Hoenig

Table 2: Question texts

Question/instruction - professions

Variable        German text                                   English translation (as used below)

                Ich werde Ihnen nun einige Berufe             I will now read out a few professions.
                vorlesen. Bitte sagen Sie mir jeweils, ob     Please tell me if there is someone in your
                Sie eine Person in ihrem persönlichen         social circle who currently works in this
                Umfeld kennen, die derzeit einen solchen      position. By ‘social circle’ I mean, for
                Beruf in Deutschland ausübt. Mit              instance, your partner, your family,
                persönlichem Umfeld meine ich z.B. Ihren      relatives, your friends, colleagues or other
                Partner / Ihre Partnerin, Ihre Familie oder   acquaintances.
                Verwandte, Ihre Freunde, Arbeitskollegen
                oder sonstige Bekannte.

                1 – ja                                        1 – yes

                 2– nein                                      2– no

                -98 – weiß nicht                              -98 – don’t know

                -97 – Angabe verweigert                       -97 – refused

t32600a         Kennen Sie in Ihrem persönlichen Umfeld       Does someone in your social circle work
                eine Krankenschwester oder einen              as a nurse or male nurse?
                Krankenpfleger?

t32600b         Kennen Sie persönlich einen Ingenieur         Do you know someone personally who
                oder eine Ingenieurin, die ihren Beruf in     works as an engineer in Germany?
                Deutschland ausüben?

t32600c         Kennen Sie persönlich eine/n Lager- oder      Do you know someone personally who
                Transportarbeiter/in?                         works as warehouse or transport worker?

t32600d         Kennen Sie persönlich einen Sozialarbeiter    Do you know someone personally who
                oder eine Sozialarbeiterin?                   works as a social worker?

t32600e         Kennen Sie persönlich einen Verkäufer         Do you know someone personally who
                oder eine Verkäuferin?                        works as a sales clerk?

t32600f         Kennen Sie persönlich einen Polizisten        Do you know someone personally who
                oder eine Polizistin?                         works as a police officer?

t32600g         Und kennen Sie persönlich einen Arzt          Do you know someone personally who
                oder eine Ärztin, die in Deutschland ihren    works as a doctor/physician?

NEPS Survey Paper No. 23, 2017                                                                      Page 15
Schulz, Horr, & Hoenig

                Beruf ausüben?

t32600h         Kennen Sie persönlich einen                 Do you know someone personally who
                Bankkaufmann oder eine Bankkauffrau?        works as a banker?

t32600k         Kennen Sie persönlich einen                 Do you know someone personally who
                Kraftfahrzeugmechaniker oder eine           works as a car mechanic?
                Kraftfahrzeugmechanikerin?

t32600l         Kennen Sie persönlich einen Juristen oder   Do you know someone personally who
                eine Juristin, wie z.B. einen Anwalt oder   works as a legal practitioner (e.g. lawyer
                eine Anwältin oder einen Richter oder       or judge)
                eine Richterin?

t32600m         Kennen Sie persönlich einen Optiker oder    Do you know someone personally who
                eine Optikerin?                             works as an optician?

t32600n         Kennen Sie persönlich einen Übersetzer      Do you know someone personally who
                oder eine Übersetzerin?                     works as a translator?

t32600o         Und zuletzt: Kennen Sie persönlich einen    And finally, do you know someone
                Grund-, Haupt- oder Realschullehrer oder    personally who works as teacher at an
                eine Grund-, Haupt- oder                    elementary school, a Hauptschule or a
                Realschullehrerin?                          Realschule?

Question/instruction follow-up question - country of origin for each profession

Variable        German text                                 English translation in the following

                Aus welchem Land stammt diese Person?       What country does he/she come from?

                [Herkunftsland]                             [country of origin]

                -98 – weiß nicht                            -98 – don’t know

                -97 – Angabe verweigert                     -97 – refused

NEPS Survey Paper No. 23, 2017                                                                     Page 16
Schulz, Horr, & Hoenig

4. Empirical Results across the NEPS cohorts
In this section, we describe the data as available as NEPS Scientific Use Files at this point.
Due to systematic differences in sample selection and survey modes, differences across
cohorts have to be interpreted with care. We therefore limit direct comparisons to parents
of cohort 2 and 4 and comparisons within a cohort, where already available.

Respondent’s migration background is defined as being born abroad or having at least one
parent or grandparent who was born abroad. Contact’s migration background is defined as
given in the question text (see Table 2 above).

We further describe respondents’ networks using the number of persons indicated, the
mean occupational status and the mean share of migrants. For the occupational status, we
allocated status values to each of the 13 professions using the International Socio-Economic
Index of Occupational Status (ISEI 88). The mean occupational status is calculated as an
average over all professions, not only over the contacts an individual respondent has. This
results in low mean values as not knowing someone with a certain profession contributes an
occupational status value of zero to the calculation. In consequence, the measurement
captures both the extensity of a person’s network, as knowing a member of a profession will
always increase the indicator, and the network’s status composition, as professions with a
higher occupational status lead to a higher increase. Engineer, social worker, doctor, legal
practitioner, translator and teacher are positions with an occupational status above the
average of 57 points of all professions in the NEPS position generator. Nurse, transport
worker, sales assistant, police officer, banker, mechanic and optician have an occupational
status below the average of all 13 professions.

As nonresponse within the position generator due to “don’t know”- or “refused”-replies is
very low for all items in the available NEPS studies (with an overall average of 0.25% for all
contact-items and 0.36% for all ethnic background items and no outliers), we display the
number of valid responses for each item but omitted additional analyses.

As a last step, we use the information from a first repeated measurement of the position
generator in NEPS waves 1 and 5 in cohort 6 (adults) to examine between- and within-
variation.

Table 3 displays case numbers and percentages for both parts of the instrument: how many
respondents answered that they personally know a person with that profession and if they
do, how many of those have a migration background? Contacts differ strongly for the
different professions. We also included the ISEI values assigned to the different occupations
to allow replication.

NEPS Survey Paper No. 23, 2017                                                          Page 17
Table 3: Descriptive values for cohorts 2, 4, 5 and 6
                                                                                                                                                                                                      Schulz, Horr, & Hoenig

                                                                   Parents SC2                  Parents SC4                   Target SC5             Target SC6, wave 1       Target SC6, wave 5

NEPS Survey Paper No. 23, 2017
                                                             N          %         %        N         %          %       N          %        %        N       %        %      N        %        %
                                  profession        ISEI   valid      cont.      mig.    valid      cont.      mig.   valid      cont.     mig.    valid    cont.    mig.   valid   cont.     mig
                                  Nurse              38     6924      83.67      12.25   9168      80.46      9.81    10664     73.64       6.54   11629   71.33    7.04    10637    78.03     6.35
                                  Engineer           73     6916      72.19      10.16   9162      67.54      8.15    10655     68.63       7.12   11611   68.24    5.90    10625    75.32     5.36
                                  Transp. worker     29     6913      47.85      18.70   9147      51.23      15.93   10625     34.75      13.59   11610   45.29    13.25   10625    50.28    13.24
                                  Social worker      65     6920      62.95       7.54   9152      57.98      5.83    10648     52.10       4.77   11612   48.94    4.61    10622    56.33     4.69
                                  Sales assistant    43     6918      77.97      13.23   9157      80.74      9.93    10648     64.59       8.44   11612   69.36    8.36    10631    74.12     8.70
                                  Police officer     56     6923      63.11       4.33   9161      66.05      2.70    10635     52.80       2.79   11612   57.28    1.69    10634    64.62     2.15
                                  Doctor             80     6922      73.13      12.50   9160      70.75      11.29   10629     57.47       7.69   11619   64.39    7.41    10634    70.96     8.48
                                  Banker             56     6923      68.16       7.40   9160      67.31      5.21    10637     63.49       4.69   11618   63.52    3.43    10634    67.34     3.55
                                  Mechanic           34     6918      61.48      16.49   9162      68.66      12.08   10625     48.27       8.81   11612   62.66    9.70    10628    65.44     9.34
                                  Legal pract.       85     6924      60.96       7.77   9161      57.18      5.09    10636     45.43       5.94   11617   53.65    3.56    10633    58.89     3.47
                                  Optician           48     6923      28.27       7.72   9163      33.21      5.01    10592     19.17       4.41   11611   27.77    3.26    10633    31.42     3.30
                                  Translator         68     6923      31.56      47.84   9165      29.72      41.45   10545     22.87      29.89   11619   24.80    36.28   10636    27.21    36.95
                                  Teacher            66     6923      76.83       6.21   9167      72.19      4.74    10655     76.07       3.80   11617   68.34    3.09    10636    72.36     3.34
                                  Average            57     6926      62.16      13.24   9160      61.77      10.56   10630     52.25       8.35   11615   55.81    8.28    10631    60.95     8.38

Page 18
Schulz, Horr, & Hoenig

Exemplarily, Figure 2 displays the average number of contacts with the given professions for
natives and migrants in cohort 2. Migrants differ significantly in how often they personally
know someone for all professions except sales assistant. The share of contacts with a
migration background is substantially larger among immigrants than among natives—for
almost all professions. While not surprising, translator is an exception as it is the only
profession for which a substantial number of natives’ contacts has a migration background.
Detailed     figures    for     cohort    4,   5     and     6     can    be    found     in

                                                                   Share of contacts
                                                             Natives                                                    Migrants
        Legal practitioner                                            0.58                                                   0.53***
                    Doctor                                                    0.71                                                   0.70
                 Engineer                                                    0.69                                                0.61***
               Translator                           0.27                                                                 0.43***
                  Teacher                                                      0.74                                               0.63***
            Social worker                                             0.58                                                     0.56
             Police officer                                                  0.69                                            0.52***
                   Banker                                                    0.70                                              0.57***
                  Optician                              0.35                                                       0.28***
           Sales assistant                                                          0.81                                                  0.81
                     Nurse                                                          0.81                                                 0.78*
                Mechanic                                                     0.69                                                   0.68
        Transport worker                                         0.50                                                          0.57***

                                0     .1 .2      .3 .4 .5 .6            .7 .8 .9           1        0    .1 .2   .3 .4 .5 .6 .7        .8 .9     1

                                          Share of contacts with mig. background
                                                             Natives                                                    Migrants
        Legal practitioner           0.01                                                                        0.22***
                    Doctor             0.05                                                                            0.37***
                 Engineer             0.03                                                                           0.33***
               Translator                            0.29                                                                              0.72***
                  Teacher            0.01                                                                        0.22***
            Social worker             0.02                                                                       0.22***
             Police officer          0.01                                                                    0.14***
                   Banker            0.01                                                                          0.25***
                  Optician            0.02                                                                      0.20***
           Sales assistant             0.04                                                                            0.35***
                     Nurse            0.03                                                                               0.39***
                Mechanic               0.04                                                                                0.44***
        Transport worker                0.07                                                                                 0.48***

                                0     .1 .2      .3 .4 .5 .6            .7 .8 .9           1        0    .1 .2   .3 .4 .5 .6 .7        .8 .9     1

    SC4, Parents; P-values of two-tailed chi2-test, comparing groups: * p
Schulz, Horr, & Hoenig

Figure                                                                                                                                                   5,

                                                                   Share of contacts
                                                             Natives                                                     Migrants
        Legal practitioner                                     0.45                                                         0.47
                    Doctor                                            0.58                                                       0.56
                 Engineer                                                    0.69                                                    0.66*
               Translator                        0.22                                                                0.29***
                  Teacher                                                         0.77                                                 0.69***
            Social worker                                          0.53                                                       0.50*
             Police officer                                         0.54                                                    0.46***
                   Banker                                                  0.64                                                   0.59***
                  Optician                      0.20                                                            0.17*
           Sales assistant                                                 0.64                                                       0.67*
                     Nurse                                                     0.74                                                    0.70***
                Mechanic                                         0.49                                                       0.46*
        Transport worker                                0.34                                                             0.40***

                                0     .1 .2      .3 .4 .5 .6            .7 .8 .9          1         0   .1 .2     .3 .4 .5 .6 .7       .8 .9     1

                                          Share of contacts with mig. background
                                                             Natives                                                     Migrants
        Legal practitioner            0.03                                                                       0.21***
                    Doctor            0.04                                                                          0.28***
                 Engineer             0.03                                                                           0.29***
               Translator                        0.22                                                                            0.58***
                  Teacher            0.02                                                                    0.15***
            Social worker             0.03                                                                   0.16***
             Police officer          0.01                                                                  0.12***
                   Banker            0.02                                                                     0.19***
                  Optician           0.02                                                                    0.16***
           Sales assistant             0.04                                                                       0.29***
                     Nurse            0.03                                                                       0.26***
                Mechanic              0.04                                                                           0.35***
        Transport worker                0.08                                                                           0.39***

                                0     .1 .2      .3 .4 .5 .6            .7 .8 .9          1         0   .1 .2     .3 .4 .5 .6 .7       .8 .9     1

    SC5, Target; P-values of two-tailed chi2-test, comparing groups: * p
Schulz, Horr, & Hoenig

Figure                                                                                                                                                 6,

                                                                  Share of contacts
                                                            Natives                                                       Migrants
                     Nurse                                                0.69                                                    0.60***
                 Engineer                                                 0.68                                                  0.57***
        Transport worker                                     0.44                                                          0.45
            Social worker                                       0.50                                                     0.40***
           Sales assistant                                               0.69                                                        0.66
             Police officer                                         0.58                                                 0.39***
                    Doctor                                              0.66                                                     0.56***
                   Banker                                              0.63                                                  0.48***
                Mechanic                                              0.62                                                        0.59
        Legal practitioner                                         0.55                                                   0.41***
                  Optician                          0.31                                                          0.21***
               Translator                        0.23                                                                 0.30***
                  Teacher                                                    0.72                                               0.54***

                                0    .1 .2       .3 .4 .5 .6           .7 .8 .9          1         0      .1 .2   .3 .4 .5 .6 .7       .8 .9   1

                                         Share of contacts with mig. background
                                                            Natives                                                       Migrants
                     Nurse           0.03                                                                              0.32***
                 Engineer            0.02                                                                            0.27***
        Transport worker               0.07                                                                                 0.43***
            Social worker            0.03                                                                       0.17***
           Sales assistant            0.04                                                                             0.32***
             Police officer         0.01                                                                    0.09***
                    Doctor            0.04                                                                          0.25***
                   Banker           0.01                                                                       0.16***
                Mechanic              0.05                                                                               0.37***
        Legal practitioner           0.02                                                                     0.13***
                  Optician          0.01                                                                      0.14***
               Translator                           0.28                                                                          0.59***
                  Teacher            0.01                                                                     0.14***

                                0    .1 .2       .3 .4 .5 .6           .7 .8 .9          1         0      .1 .2   .3 .4 .5 .6 .7       .8 .9   1

    SC6, Target, NEPS Wave 1; P-values of chi2-test, comparing groups: * p
Schulz, Horr, & Hoenig

                                                                  Share of contacts
                                                             Natives                                                       Migrants
        Legal practitioner                                             0.60                                                        0.55**
                    Doctor                                                    0.71                                                      0.69*
                 Engineer                                                       0.76                                                     0.70***
               Translator                          0.26                                                                  0.35***
                  Teacher                                                     0.74                                                        0.67***
            Social worker                                           0.57                                                           0.53**
             Police officer                                             0.67                                                        0.55***
                   Banker                                                0.69                                                          0.60***
                  Optician                             0.32                                                           0.27***
           Sales assistant                                                   0.74                                                          0.73
                     Nurse                                                     0.79                                                         0.75***
                Mechanic                                                  0.66                                                         0.63*
        Transport worker                                         0.50                                                           0.50

                                0    .1 .2       .3 .4 .5 .6            .7 .8 .9         1         0    .1 .2        .3 .4 .5 .6 .7        .8 .9    1

                                         Share of contacts with mig. background
                                                             Natives                                                       Migrants
        Legal practitioner            0.03                                                                         0.24***
                    Doctor            0.03                                                                       0.19***
                 Engineer                0.09                                                                          0.32***
               Translator             0.03                                                                    0.14***
                  Teacher              0.06                                                                        0.24***
            Social worker            0.01                                                                  0.07***
             Police officer            0.06                                                                       0.21***
                   Banker            0.02                                                                     0.13***
                  Optician             0.06                                                                         0.26***
           Sales assistant           0.02                                                                    0.11***
                     Nurse           0.02                                                                    0.10***
                Mechanic                              0.32                                                                         0.55***
        Transport worker             0.02                                                                    0.11***

                                0    .1 .2       .3 .4 .5 .6            .7 .8 .9         1         0    .1 .2        .3 .4 .5 .6 .7        .8 .9    1

    SC6, Target, NEPS Wave 5; P-values of two-tailed chi2-test, comparing groups: * p
Schulz, Horr, & Hoenig

                                                                   Share of contacts
                                                             Natives                                                         Migrants
        Legal practitioner                                               0.63                                                     0.54***
                    Doctor                                                      0.74                                                      0.70**
                 Engineer                                                       0.75                                                   0.65***
               Translator                           0.28                                                                     0.43***
                  Teacher                                                        0.80                                                   0.67***
            Social worker                                                0.64                                                       0.59***
             Police officer                                                0.68                                                 0.49***
                   Banker                                                    0.72                                                  0.57***
                  Optician                            0.30                                                         0.22***
           Sales assistant                                                       0.78                                                        0.79
                     Nurse                                                          0.85                                                      0.80***
                Mechanic                                                0.62                                                        0.59**
        Transport worker                                       0.47                                                              0.52***

                                0     .1 .2      .3 .4 .5 .6            .7 .8 .9          1         0    .1 .2     .3 .4 .5 .6 .7          .8 .9     1

                                          Share of contacts with mig. background
                                                             Natives                                                         Migrants
        Legal practitioner           0.02                                                                            0.28***
                    Doctor            0.05                                                                              0.36***
                 Engineer            0.02                                                                                0.36***
               Translator                               0.34                                                                               0.72***
                  Teacher            0.01                                                                           0.24***
            Social worker             0.02                                                                          0.24***
             Police officer          0.01                                                                        0.17***
                   Banker            0.01                                                                             0.29***
                  Optician            0.03                                                                           0.27***
           Sales assistant             0.04                                                                               0.39***
                     Nurse            0.02                                                                                  0.42***
                Mechanic               0.05                                                                                      0.52***
        Transport worker                0.06                                                                                    0.51***

                                0     .1 .2      .3 .4 .5 .6            .7 .8 .9          1         0    .1 .2     .3 .4 .5 .6 .7          .8 .9     1

    SC2, Parents; P-values of two-tailed chi2-test, comparing groups: * p
Schulz, Horr, & Hoenig

Table 4 displays summary statistics for all cohorts. With a range of 6.58 to 7.67 contacts,
migrants have significantly fewer contacts than natives in each cohort (6.79 to 8.26
contacts). Their contacts also have a significantly lower mean occupational prestige with a
range of 28.72 to 33.21 points (compared to 30.035 to 36.52 points for natives). Most
striking is the difference in the average share of contacts with a migration background. While
natives only have a 3-4% share of contacts who are not from Germany among all
occupations, numbers for migrants are multiple times higher with range from 22% to 38%.
Unsurprisingly, we observe the smallest differences in the mean ISEI and the number of
persons indicated for academic students (cohort 5).

Figure 3 illustrates the relationship between respondent’s own occupational prestige (if they
are employed) and their contact’s average occupational prestige for cohorts 2 and 4. Figure
4 displays the corresponding relationship for both waves in cohort 6. As theory suggests, we
see a positive relationship, both for Natives and Migrants and over cohorts. As cohort 5 is
based on a sample of academic students, showing the relationship between respondent’s
own occupational prestige and the average prestige of their networks is less insightful and
we therefore omitted a graph for this cohort.

NEPS Survey Paper No. 23, 2017                                                          Page 24
Table 4: Summary Statistics for cohorts 2, 4, 5 and 6
                                                                                                                                                                                               Schulz, Horr, & Hoenig

                                                                          Parents SC2                Parents SC4                Target SC5          Target SC6, wave 1    Target SC6, wave 5

NEPS Survey Paper No. 23, 2017
                                                                                      N                         N                            N                    N                     N
                                                                     Average        valid       Average        valid      Average        valid     Average       valid   Average       valid

                                                        Natives           8.26      5134             8.11      7359             6.79     8919          7.40      9405        8.00      8774
                                  Number of
                                                       Migrants        7.55***      1792         7.67***       1811          6.58**      1784       6.60***      2225     7.50***      1806
                                  persons indicated
                                                         Total            8.08      6926             8.02      9170             6.75     10703         7.25     11630        7.95     10580

                                                        Natives          36.52      5134            35.36      7359           30.35      8919         32.50      9405       35.15      8774
                                  Contact’s Mean
                                                       Migrants      33.13***       1774        33.21***       1811         29.46**       336      28.72***      2225    32.99***      1806
                                  ISEI
                                                         Total           35.64      6926            34.94      9170           30.20      10703        31.78     11630       34.78     10580

                                                        Natives           0.04      5126             0.04      7267             0.03     8815          0.03      9303        0.04      8712
                                  Mean share of
                                                       Migrants        0.38***      1792         0.36***       1783         0.26***      1750       0.27***      2166     0.22***      1790
                                  migrants
                                                         Total            0.13      6920             0.10      9050             0.07     10565         0.08     11469        0.07     10502

                                  P-values of two-tailed t-test, comparing values between Natives and Migrants: * p
Schulz, Horr, & Hoenig

                                                     SC2, Parents
                                 Natives                                         Migrants
        60
        40
        20
          0

                 20       40        60          80         100   20         40      60      80   100
                                           respondent's occupational prestige

                                                     SC4, Parents
                                 Natives                                         Migrants
        60
        40
        20
          0

                 20       40        60          80         100   20         40      60      80   100
                                           respondent's occupational prestige

Figure 3: SC2 and SC3, metric relationship between respondent's own occupation prestige
(ISEI) and the average occupational prestige of their contacts. Jittered display of data points;
lowess based on a locally weighted regression.

NEPS Survey Paper No. 23, 2017                                                                     Page 26
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